# AFN: Adaptive Fusion Normalization via an Encoder-Decoder Framework

Source: [AFN: Adaptive Fusion Normalization via an Encoder-Decoder Framework](<https://www.sota2.com/research/paper/68fa72381e209131cea5507b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:20:14.513Z

Authors: Zikai Zhou, Shuo Zhang, Ziruo Wang, Huanran Chen

Published: 2023-08-07T06:08:51.000Z

## Abstract

The success of deep learning is inseparable from normalization layers. Researchers have proposed various normalization functions, and each of them has both advantages and disadvantages. In response, efforts have been made to design a unified normalization function that combines all normalization procedures and mitigates their weaknesses. We also proposed a new normalization function called Adaptive Fusion Normalization. Through experiments, we demonstrate AFN outperforms the previous normalization techniques in domain generalization and image classification tasks.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2308.03321>)

[PDF](<https://arxiv.org/pdf/2308.03321.pdf>)